Article excerpt

This book is an impressive collection of essays emanating from a 1998 conference held at Middlebury College, Vermont, U.S.A. The authors address the impact of the "new science of complexity" on economics and its teaching.

In his introduction, David Colander explains that like all sciences, complexity involves the search for simplicity. However, complexity theory searches for simplicity not in the underlying structures of reality, but in the iterative and dynamic processes that create complex phenomena. In this and other respects, complexity theory complements and enhances, rather than replaces, much of existing science.

Perhaps because this volume addresses the teaching of economics in the light of complexity, as well as complexity itself, most of the essays adopt a clear, presentational style. The first part of the book serves largely to introduce some of the key ideas. In Chapter one, W. Brian Arthur discusses some of the basic elements of complexity thinking: increasing returns, positive feedback, out-of-equilibrium states, and emergent properties. In Chapter two, William Brock introduces some further key concepts, such as scaling laws, self-similarity and self-organised criticality. Brian Arthur returns in the third chapter to address the more specific issue of cognition in economics. This chapter takes a different but complementary tack, discussing how we attribute meaning to complex phenomena. Frederic Pryor takes an imaginary and entertaining view of complexity theory from the year 2028.

Two essays in the second part of the book discuss the implications for economic policy. In chapter five, William Brock and David Colander show that arguments for both laissez faire and government intervention can be derived from the literature on complexity. They make the important point that the complexity vision shifts the debate over policy from deductive models to empirically and historically grounded studies. In contrast, Roger Koppl argues in the following chapter that complexity theory, insofar as it reflects the insights of Austrian school economics, implies that the role of the state in the economy should be highly limited.

The remainder of this volume addresses general and specific issues involved in the teaching of complexity ideas. The contributions here are by David Colander, Roger Koppl, James Stodder, Duncan Foley, Robert Prasch, Kevin Hoover, Sunder Ramaswamy, J. Barkley Rosser, Peter Hans Matthews, Stephen Magee and Michael Rothschild. These essays contain many insights on how students can be taught to use data constructively, rather than rely on pure deduction. It is argued that students should be taught to discern patterns and regularities in data, and then seek explanations using insights from complexity theory and elsewhere.

For this reviewer, the most interesting essays in this latter half of the book are by Kevin Hoover (which contains an insightful discussion of the relationship between macro and micro) and by Barkley Rosser (who makes the point that earlier alleged "fads" such a catastrophe theory and chaos theory are linked to complexity theory by a common emphasis on disequilibria and nonlinearities).

Interestingly, both David Colander and Michael Rothschild discuss why the "learning curve"--a concept widely familiar to business people, managers and others--is absent from the economics texts. The learning curve expresses the idea that productivity increases because people learn through time. Yet this is a dynamic concept, undermining static equilibrium and undermining standard views on competition. It is arguably for these reasons that it is neglected.

Overall, this is a very useful volume because it simultaneously attempts to elaborate the "complexity vision" and to consider how ideas on complexity can be taught to students of economics. …